Non-local image denoising method based on low rank restoration

Abstract

The invention discloses a non-local image denoising method based on low rank restoration. The method comprises the steps of (1) collecting an image, (2) carrying out gray transformation on the collected image, (3) establishing a three-dimensional similarity matrix through the global search of a similar pixel block for the image which is subjected to gray transformation, then carrying out hard threshold filtering on the discrete cosine transform and Hadamard transform coefficients of the three-dimensional matrix is carried out, obtaining the initial estimation of the similar pixel block with the removal of partial noise, and improving the matching accuracy of the similar pixel block with low rank restoration denoising while removing a large part of noise, and (4) with transform domain filtering as prior knowledge, searching a similar pixel block in a search area for an initially denoised reference pixel block, then forming the similarity matrix by using a similarity block corresponding to an original image, carrying out low rank matrix decomposition on the similarity matrix, effectively separating noise and a signal, and obtaining a final denoised image. The method has the advantages of a simple and fast algorithm, a high signal-to-noise ratio and good consistency and is especially suitable for the requirements of high quality noise reduction of large-scale images.

Description

technical field [0001] The invention relates to a non-local image noise removal method, which belongs to the technical field of image processing. Background technique [0002] Image noise is a ubiquitous phenomenon, which is largely caused by the unavoidable interference of imaging systems, transmission media, and images in the process of acquisition, transmission, and recording, which can easily lead to image quality degradation and hinder image quality. The intuitive image expression, even directly affects the success or failure of key technologies such as feature extraction and target recognition. So it is necessary to denoise the image. On the one hand, it reduces noise while protecting boundaries and details, which has a good effect on subsequent image analysis. [0003] Studies have shown that most of the noise can be represented by Gaussian white noise, impulse noise or a mixture of the two noises. At present, image denoising methods mainly include local methods (m...

AI Technical Summary

Problems solved by technology

The traditional low-rank representation uses a singular value hard threshold to minimize the nuclear norm, without considering the difference in the similarity of pixel blocks, which is prone to streak distortion and the denoising effect is not ideal

Images